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<@ULVA73B9P> how to deploy code in an environment ...
# ask-marvin
m
@Marvin how to deploy code in an environment where the dependencies are not installed. I am deploying using a flow file.
m
To deploy a flow in an environment where dependencies are not installed, you can leverage Prefect's capability to manage dependencies at runtime. Prefect allows you to specify Python packages that should be installed in the container where your flow runs. Here's a concise guide on how to achieve this: 1. Use Prefect Managed Infrastructure: Prefect Cloud can run your flows on managed infrastructure, which handles the execution environment for you. This means you don't need to set up infrastructure or have dependencies pre-installed. More details can be found here. 2. Specify Dependencies: You can specify the Python packages required for your flow in the "Pip Packages" field in the Prefect UI or via the deployment configuration. These packages will be installed in the execution environment at runtime. 3. Create a Deployment: Use the 
deploy
method or a 
prefect.yaml
configuration to create a deployment. This deployment can be associated with a work pool that dynamically provisions the necessary infrastructure. More details on deployments can be found here. By using these features, you can run your flows without pre-installing dependencies on the target environment. If you have further questions or need more specific examples, feel free to ask!
m
@Marvin I would like to just use have a single command to deploy a flow and have it inherit the config and parameters like: "name": "data_extraction", "work_pool_name": "cloud-run-push", "cron": "0 1 * * *", "description": "Daily extract of data from multiple sources", "parameters": { "extraction_configs": [ ]}
m
To deploy a flow with a single command while inheriting configuration and parameters in Prefect 3.x, you can use the 
deploy
method with specific parameters. Here's a concise way to do it: 1. Define the Deployment in Code: You can define your deployment directly in your Python code using the 
deploy
method. This allows you to set default parameters, job variables, and other configurations. 2. Use the 
deploy
Method: - You can specify default parameters using the 
parameters
keyword argument. - Use the 
job_variables
keyword to override any infrastructure-related settings. Here's a basic example of how you might structure your deployment:
Copy code
python
from prefect import flow

@flow
def data_extraction_flow():
    # Your flow logic here
    pass

# Deploy the flow
data_extraction_flow.deploy(
    name="data_extraction",
    work_pool_name="cloud-run-push",
    cron="0 1 * * *",
    description="Daily extract of data from multiple sources",
    parameters={
        "extraction_configs": []
    }
)
This method allows you to encapsulate all the deployment configurations within your Python code, making it easy to manage and update. For more details on deployment configuration, you can refer to the Prefect documentation on deploying via Python. If you need further customization or have additional questions, feel free to ask!
m
@Marvin In such case and running prefect deploy, it will start asking for things like ? Deployment name (default): which i dont want as it is defined in the code.
m
To deploy a flow without interactive prompts using Prefect, you can leverage the 
prefect.yaml
file to define your deployment configuration. This allows you to automate the deployment process without being prompted for input. Here's how you can set it up: 1. Create a 
prefect.yaml
File: Define your deployment configuration in a 
prefect.yaml
file. This file will contain all the necessary parameters, such as the deployment name, work pool, schedule, and any other settings. 2. Run 
prefect deploy
: When you run 
prefect deploy
in a directory containing a 
prefect.yaml
file, Prefect will use the configurations specified in the file, thus bypassing interactive prompts. Here's a basic example of what your 
prefect.yaml
file might look like:
Copy code
yaml
name: data_extraction
work_pool_name: cloud-run-push
cron: "0 1 * * *"
description: Daily extract of data from multiple sources
parameters:
  extraction_configs: []
3. Automate the Deployment: By running 
prefect deploy
in the directory with the 
prefect.yaml
file, the deployment will be registered without requiring further input. This approach ensures that all configurations are inherited from the file. For more details on using 
prefect.yaml
, you can refer to the Prefect documentation on deployment mechanics. This method should streamline your deployment process and eliminate the need for manual input. If you have more questions, feel free to ask!
m
@Marvin, but that was not preferred by prefect right? they recommend using the prefect deploy within a flow file.
m
In Prefect 3.x, both using a 
prefect.yaml
file and the 
deploy
method within a flow file are supported for deployments, but they serve slightly different use cases: 1. Using 
prefect.yaml
: - This method is flexible and allows you to define multiple deployment configurations within a single file. It's useful for managing multiple deployments with different settings in the same project. - Prefect 3.x allows the 
prefect.yaml
file to be located in various directories, not just the root, and it can be named differently as long as it ends in 
.yaml
. - The 
prefect deploy
command will use this file to automate deployments without interactive prompts. More details can be found here. 2. Using the 
deploy
Method in a Flow File: - This is a more programmatic approach where deployment logic is embedded directly in your Python code. It allows for dynamic configuration directly within the flow's logic. - It's a convenient way to define deployment parameters and schedules within the flow itself, allowing for tighter integration with the code. - This method is particularly useful if you prefer to manage deployments alongside your flow logic. Both methods are valid, and the choice largely depends on your workflow preferences and project requirements. If you prefer a more code-centric approach, using the 
deploy
method within your flow file is recommended. If you need to manage multiple deployment configurations or want to separate deployment logic from code, the 
prefect.yaml
approach might be more suitable.
m
hi @Marvin, if i have a deployment that i'd like to schedule with two different parameters and on two different schedules. is that possible?
m
✅ thought for 7.3 seconds
Yes, that's entirely possible in Prefect. You can create multiple schedules for the same deployment, each with different parameters. Here's how you can do it:
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python
from prefect import flow
from datetime import timedelta

@flow
def my_flow(param1: str, param2: int):
    print(f"Running with param1={param1}, param2={param2}")

# Create the deployment with multiple schedules
flow.deploy(
    name="multi-schedule-deployment",
    schedules=[
        # First schedule - runs daily with one set of parameters
        {
            "schedule": timedelta(days=1),
            "parameters": {"param1": "first config", "param2": 1}
        },
        # Second schedule - runs weekly with different parameters
        {
            "schedule": timedelta(weeks=1),
            "parameters": {"param1": "second config", "param2": 2}
        }
    ]
)
Each schedule in the list can have its own: - Schedule definition (using intervals, cron expressions, or other schedule types) - Unique set of parameters - Tags - Job configuration You can also manage these schedules through the Prefect UI, where you can pause, resume, or modify individual schedules without affecting the others. A few tips: 1. Each schedule runs independently, so they won't interfere with each other 2. You can mix different types of schedules (cron, interval, etc.) 3. You can add or remove schedules after deployment creation through the API or UI Would you like me to show you examples of other schedule types or how to manage these schedules through the UI?
m
@Marvin how to do multiple schedules with different paramters when deploying using prefect.yaml?
m
✅ thought for 8.1 seconds
I'll help you set up multiple schedules with different parameters in your 
prefect.yaml
file. Here's how to structure it:
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yaml
deployments:
  - name: my-deployment
    entrypoint: path/to/your/script.py:flow_function
    schedules:
      # First schedule
      - schedule: "0 9 * * *"  # Runs at 9 AM daily
        parameters:
          param1: "first config"
          param2: 100
        
      # Second schedule
      - schedule: "0 15 * * MON"  # Runs at 3 PM every Monday
        parameters:
          param1: "second config"
          param2: 200

      # You can add more schedules as needed
      - schedule: 
          interval: 3600  # Runs every hour (specified in seconds)
          timezone: "America/New_York"  # Optional timezone
        parameters:
          param1: "third config"
          param2: 300
The schedules can use different formats: 1. Cron expressions (like 
"0 9 * * *"
) 2. Intervals (in seconds) 3. RRule strings (for more complex recurring schedules) Each schedule block can include: - `schedule`: The timing definition - `parameters`: The specific parameters for that schedule - `timezone`: Optional timezone specification - `tags`: Optional list of tags for the scheduled runs To deploy this configuration, simply run:
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bash
prefect deploy
If you need to update the schedules later, you can modify the 
prefect.yaml
file and redeploy, or manage them through the Prefect UI. Would you like to see examples of more complex schedule patterns or parameter configurations?
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